Founding Platform & AI Systems Engineer
Core Systems Architecture · Multi-Tenant Infrastructure · AI Safety Systems
We are hiring a Founding Engineer to help build the core technical foundation of the JurneeGo platform. This is a system ownership role, not just feature development.
The essentials, up front
Role
Founding Platform & AI Systems Engineer
Location
Vietnam (Hybrid or Remote within Vietnam preferred)
Seniority
Founding Engineer
Stack
AI systems engineering or Platform / DevOps engineering; cloud environments (AWS preferred); multi-tenant systems and RBAC
Reports to
Head of Engineering
Hiring process
Submit your CV or LinkedIn profile with a short introduction; an optional engineering thought exercise helps start deeper technical conversations.
A child-safe AI learning product
JurneeGo is building a safe AI learning platform for children that connects children, parents, and teachers inside a transparent and governed AI environment.
Most AI systems today were not designed for children. Children are either locked out entirely, or exposed to tools that were never designed for learning environments.
JurneeGo solves this by combining:
Our mission is to build the technical infrastructure that makes AI safe and usable in education environments.
Tiếng Việt: JurneeGo đang xây dựng nền tảng AI học tập an toàn cho trẻ em, kết nối trẻ em, phụ huynh và giáo viên trong một môi trường AI minh bạch và có kiểm soát. Sứ mệnh của chúng tôi là xây dựng hạ tầng kỹ thuật giúp AI có thể được sử dụng an toàn trong giáo dục.
We are hiring a Founding Engineer to help build the core technical foundation of the JurneeGo platform. This is a system ownership role, not just feature development.
You will work directly with the Head of Engineering to design and implement core backend systems, AI orchestration layers, safety enforcement logic, and platform reliability foundations.
We are looking for engineers who think in systems and architecture, not just tasks. You may be strongest in either:
Both profiles are valuable. What matters most is system-level thinking and ownership.
Tiếng Việt: Đây là vị trí sở hữu hệ thống, không chỉ phát triển tính năng. Ứng viên có thể mạnh về Hệ thống AI hoặc Platform / DevOps — cả hai hướng đều phù hợp. Điều quan trọng nhất là tư duy hệ thống và tinh thần ownership.
Design scalable backend architecture
Domain models, APIs, and state management built to scale.
Build secure multi-tenant services
Strict boundary enforcement between tenants.
Implement governance and permission systems
Governance logic and permission systems across the platform.
Maintain high-integrity audit logging
Audit logging and data visibility layers you can trust.
Implement AI orchestration pipelines
The core pipelines that route and coordinate AI work.
Design prompt packaging & context management
Systems for prompt packaging and context management.
Implement guardrails and safety enforcement
Guardrails and safety enforcement logic around the model.
Build evaluation & monitoring pipelines
Evaluation and monitoring pipelines for AI responses.
Optimize reliability and cost of AI usage
Tune for both stability and cost across AI usage.
Design CI/CD pipelines and release processes
Reliable pipelines and a clean release workflow.
Implement infrastructure-as-code
Infrastructure-as-code and environment management.
Build observability systems
Logs, metrics, and tracing across the platform.
Improve reliability, deployment safety & cloud cost governance
Raise reliability and deployment safety while governing cloud cost.
Must-haves and good to have
Must-haves
- Strong system-level thinker, not only feature-focused.
- Experience designing backend or distributed systems.
- Comfortable designing multi-tenant systems and RBAC models.
- Experience working with cloud environments (AWS preferred).
- Deep strength in AI systems OR Platform/DevOps.
- High ownership mindset and comfortable operating with ambiguity.
- AI-native engineer using modern AI coding tools effectively.
What we are not screening for
- A pure frontend engineer (no).
- Enterprise DevOps only (too slow, too rigid).
- A research AI engineer (too theoretical).
- A "prompt engineer" (useless at this stage).
Competitive salary
Competitive salary based on experience.
Meaningful equity
Meaningful equity participation, 0.5% to 1.5%.
Founding-level influence
Founding-level influence on system architecture, direct collaboration with the Head of Engineering, and the opportunity to shape long-term technical direction.
How we get to yes
First 30 days
Goal: understand the system deeply. Review architecture, map data flow, identify risks. Deliverable: a "System Risk Map."
60 days
Goal: own the infrastructure layer. Improve API structure, optimize AI routing, implement logging + monitoring. Deliverable: a stable backend system.
90 days
Goal: become system co-architect. Improve performance, reduce costs, lead backend decisions. Deliverable: a scalable, production-ready system.
Send us a short email
We encourage candidates to include links to systems they have built or owned. This could include GitHub repositories, production systems, infrastructure projects, or technical write-ups describing systems you helped design.
Contact: careers@jurneego.com
To help us understand how you think about systems, we invite interested candidates to answer the question below. This is not a formal test and there is no single correct answer. We are interested in your reasoning and architecture thinking.
Question: Describe the architecture you would design to build a safe multi-tenant AI platform used by children, parents, and teachers.
Your design should consider:
You may answer with a short written explanation, a diagram or architecture sketch, or a system breakdown of components.
We are primarily evaluating system thinking, safety awareness, and clarity of architecture. Submissions are optional but often helpful for starting deeper technical conversations.
Build AI that helps kids think.
Early builders, high ownership, and a care bar set by children and families.